# tensorplay._composite_funcs.quantile

Source: https://www.tensorplay.cn/docs/generated/tensorplay._composite_funcs.quantile.html

```python
tensorplay._composite_funcs.quantile(input, q, dim=None, keepdim=False, *, interpolation='linear', out=None) → Tensor
```

Computes the q-th quantiles of each row of the input tensor along the dimension dim.

To compute the quantile, we map q in [0, 1] to the range of indices [0, n] to find the location
of the quantile in the sorted input. If the quantile lies between two data points a < b with
indices i and j in the sorted order, result is computed according to the given
interpolation method as follows:

- linear: a + (b - a) * fraction, where fraction is the fractional part of the computed quantile index.

- lower: a.

- higher: b.

- nearest: a or b, whichever’s index is closer to the computed quantile index (rounding down for .5 fractions).

- midpoint: (a + b) / 2.

If q is a 1D tensor, the first dimension of the output represents the quantiles and has size
equal to the size of q, the remaining dimensions are what remains from the reduction.

> **Note**
>
> By default dim is None resulting in the input tensor being flattened before computation.

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the input tensor.

- q ([float](https://docs.python.org/3/builtins/functions.html#float) or [Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – a scalar or 1D tensor of values in the range [0, 1].

- dim ([int](https://docs.python.org/3/builtins/functions.html#int), optional) – the dimension to reduce. keepdim (bool, optional): whether the output tensor has dim retained or not. Default: False.

Keyword Arguments:

- interpolation ([str](https://docs.python.org/3/builtins/stdtypes.html#str), optional) – interpolation method to use when the desired quantile lies between two data points. Can be linear, lower, higher, midpoint and nearest. Default is linear.

- out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output tensor.

Example:

```
>>> a = tensorplay.randn(2, 3)
>>> a
tensor([[ 0.0795, -1.2117,  0.9765],
        [ 1.1707,  0.6706,  0.4884]])
>>> q = tensorplay.tensor([0.25, 0.5, 0.75])
>>> tensorplay.quantile(a, q, dim=1, keepdim=True)
tensor([[[-0.5661],
        [ 0.5795]],

        [[ 0.0795],
        [ 0.6706]],

        [[ 0.5280],
        [ 0.9206]]])
>>> tensorplay.quantile(a, q, dim=1, keepdim=True).shape
tensorplay.Size([3, 2, 1])
>>> a = tensorplay.arange(4.)
>>> a
tensor([0., 1., 2., 3.])
>>> tensorplay.quantile(a, 0.6, interpolation='linear')
tensor(1.8000)
>>> tensorplay.quantile(a, 0.6, interpolation='lower')
tensor(1.)
>>> tensorplay.quantile(a, 0.6, interpolation='higher')
tensor(2.)
>>> tensorplay.quantile(a, 0.6, interpolation='midpoint')
tensor(1.5000)
>>> tensorplay.quantile(a, 0.6, interpolation='nearest')
tensor(2.)
>>> tensorplay.quantile(a, 0.4, interpolation='nearest')
tensor(1.)
```